Create app.py

app.py

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+import streamlit as st
+import os
+import tempfile
+import pandas as pd
+from datasets import load_dataset
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from peft import PeftModel
+import torch
+import librosa
+import numpy as np
+import evaluate
+
+st.title("📊 Évaluation WER d'un modèle Whisper")
+st.markdown("Ce Space permet d'évaluer la performance WER d'un modèle Whisper sur un dataset audio.")
+
+# Section : Choix du modèle
+st.subheader("1. Choix du modèle")
+model_option = st.radio("Quel modèle veux-tu utiliser ?", (
+    "Whisper Large (baseline)",
+    "Whisper Large + LoRA (SimpleFrog/whisper_finetuned)",
+    "Whisper Large + LoRA + Post-processing (à venir)"
+))
+
+# Section : Lien du dataset
+st.subheader("2. Chargement du dataset Hugging Face")
+dataset_link = st.text_input("Lien du dataset (format: user/dataset_name)", value="SimpleFrog/mon_dataset")
+hf_token = st.text_input("Token Hugging Face (si dataset privé)", type="password")
+
+# Section : Bouton pour lancer l'évaluation
+start_eval = st.button("🚀 Lancer l'évaluation WER")
+
+if start_eval:
+    st.subheader("🔍 Traitement en cours...")
+
+    # 🔹 Télécharger dataset
+    with st.spinner("Chargement du dataset..."):
+        try:
+            dataset = load_dataset(dataset_link, split="test", token=hf_token)
+        except Exception as e:
+            st.error(f"Erreur lors du chargement du dataset : {e}")
+            st.stop()
+
+    # 🔹 Charger le modèle choisi
+    with st.spinner("Chargement du modèle..."):
+        base_model_name = "openai/whisper-large"
+        model = WhisperForConditionalGeneration.from_pretrained(base_model_name)
+
+        if "LoRA" in model_option:
+            model = PeftModel.from_pretrained(model, "SimpleFrog/whisper_finetuned", token=hf_token)
+
+        processor = WhisperProcessor.from_pretrained(base_model_name)
+        model.eval()
+
+    # 🔹 Préparer WER metric
+    wer_metric = evaluate.load("wer")
+
+    results = []
+
+    for example in dataset:
+        try:
+            audio_path = example["file_name"]  # full path or relative path in AudioFolder
+            reference = example["text"]
+
+            # Load audio (we assume dataset is structured with 'file_name')
+            waveform, _ = librosa.load(audio_path, sr=16000)
+            waveform = np.expand_dims(waveform, axis=0)
+            inputs = processor(waveform, sampling_rate=16000, return_tensors="pt")
+
+            with torch.no_grad():
+                pred_ids = model.generate(input_features=inputs.input_features)
+            prediction = processor.batch_decode(pred_ids, skip_special_tokens=True)[0]
+
+            # 🔹 Nettoyage ponctuation pour WER "sans ponctuation"
+            def clean(text):
+                return ''.join([c for c in text.lower() if c.isalnum() or c.isspace()]).strip()
+
+            ref_clean = clean(reference)
+            pred_clean = clean(prediction)
+            wer = wer_metric.compute(predictions=[pred_clean], references=[ref_clean])
+
+            results.append({
+                "Fichier": audio_path,
+                "Référence": reference,
+                "Transcription": prediction,
+                "WER": round(wer, 4)
+            })
+
+        except Exception as e:
+            results.append({
+                "Fichier": example.get("file_name", "unknown"),
+                "Référence": "Erreur",
+                "Transcription": f"Erreur: {e}",
+                "WER": "-"
+            })
+
+    # 🔹 Afficher le tableau de résultats
+    df = pd.DataFrame(results)
+    st.subheader("📋 Résultats de la transcription")
+    st.dataframe(df)
+
+    mean_wer = df[df["WER"] != "-"]["WER"].mean()
+    st.markdown(f"### 🎯 WER moyen (sans ponctuation) : `{mean_wer:.3f}`")
+
+    # Bloc placeholder pour post-processing à venir
+    if "Post-processing" in model_option:
+        st.info("🛠️ Le post-processing sera ajouté prochainement ici...")